Abstract
There is a problem in the field of early automatic detection of forest fire smoke that due to low concentration or tiny size, some smoke is difficult to capture. This paper proposes a multiscale convergence coordinated pyramid network with mixed attention and Fast-robust NMS (MMFNet) for fast detection of forest fire smoke. First, a multiscale convergence coordinated pyramid network is designed, which combines dual-attention feature pyramid network and coordinated convergence module. It improves the detection rate of targets of different sizes. Second, a mixed attention module is designed to focus more on the smoke in the image and enhance the extraction of horizontal and vertical features of smoke. Then a Fast-robust NMS is proposed to accelerate the convergence of bounding boxes and increase the accuracy of prediction box. Finally, a forest fire detection system of the Internet of Things using MMFNet is built. The experimental results show that our method achieves 80.72% AP, 88.52% AP50, 83.45% AP75, 46.88%AR and 154FPS, which is superior to the state-of-art forest fire smoke detection methods.